Synthesizing complex population selection criteria

ABSTRACT

System and method to determine a reduced cohort criteria, the method including: defining N selection criteria to select a cohort from among a universe of patient data; querying a patient database, by use of a processor, and by use of the N selection criteria, in order to define a full patient population; selecting a subset of size M of the N selection criteria, to produce a subset criteria; selecting a permutation of the subset criteria, to produce a permuted subset criteria in a predetermined order; for each member of the permuted subset criteria: querying the patient database by use of the member of the permuted subset criteria to produce a respective interim patient population; combining all respective interim patient populations to produce a partial patient population; and calculating, by a processor, a coverage figure of merit that compares the partial patient population to the full patient population.

BACKGROUND

1. Field of the Invention

Embodiments of the present invention generally relate to observationaltesting, and, in particular, to a system and method for post-selectiondetermination of criteria for membership in an observational test.

2. Description of Related Art

Observational studies are an important category of study designs. Forsome kinds of investigative questions (e.g., related to plasticsurgery), randomized controlled trials may not always be indicated orethical to conduct. Instead, observational studies may be the next bestmethod to address these types of questions. Well-designed observationalstudies may provide results similar to randomized controlled trials,challenging the belief that observational studies are second-rate.Cohort studies and case-control studies are two primary types ofobservational studies that aid in evaluating associations betweendiseases and exposures.

Well-designed randomized controlled trials (RCTs) have held thepre-eminent position in the hierarchy of evidence-based medicine (EBM)as level I evidence. However, RCT methodology, which was first developedfor drug trials, can be difficult to conduct for some investigations(e.g., surgical cases). Instead, well-designed observational studies,recognized as level II or III evidence, can play an important role inderiving evidence for such investigations. Results from observationalstudies are often criticized for being vulnerable to influences byunpredictable confounding factors. However, comparable results betweenobservational studies and RCTs are achievable. Observational studies canalso complement RCTs in hypothesis generation, establishing questionsfor future RCTs, and defining clinical conditions.

Observational studies fall under the category of analytic study designsand are further sub-classified as observational or experimental studydesigns. The goal of analytic studies is to identify and evaluate causesor risk factors of diseases or health-related events. Thedifferentiating characteristic between observational and experimentalstudy designs is that in the latter, the presence or absence ofundergoing an intervention defines the groups. By contrast, in anobservational study, the investigator does not intervene and rathersimply “observes” and assesses the strength of the relationship betweenan exposure and disease variable. Three types of observational studiesinclude cohort studies, case-control studies, and cross-sectionalstudies. Case-control and cohort studies offer specific advantages bymeasuring disease occurrence and its association with an exposure byoffering a temporal dimension (i.e. prospective or retrospective studydesign). Cross-sectional studies, also known as prevalence studies,examine the data on disease and exposure at one particular time point.Because the temporal relationship between disease occurrence andexposure cannot be established, cross-sectional studies cannot assessthe cause and effect relationship.

The word “cohort” is used in epidemiology to define a set of peoplefollowed over a period of time. In particular, “cohort” refers to agroup of people with defined characteristics who are followed up todetermine incidence of, or mortality from, some specific disease, allcauses of death, or some other outcome.

A well-designed cohort study can provide powerful results. In a cohortstudy, an outcome-free or disease-free study population is firstidentified by the exposure or event of interest, and then is followed intime until the disease or outcome of interest occurs. Because exposureis identified before the outcome, cohort studies have a temporalframework to assess causality and thus have the potential to provide thestrongest scientific evidence. A cohort study is particularlyadvantageous for examining rare exposures because subjects are selectedby their exposure status, and rates of disease may be calculated inexposed and unexposed individuals over time (e.g. incidence, relativerisk). Additionally, an investigator can examine multiple outcomessimultaneously. However, the cohort study may be susceptible toselection bias. A cohort study may be large, particularly to study rareexposures, and require a large sample size and a potentially longfollow-up duration of the study design, resulting in a costly endeavor.

Cohort studies may be prospective or retrospective. Prospective studiesare carried out from the present time into the future. Becauseprospective studies are designed with specific data collection methods,it has the advantage of being tailored to collect specific exposure dataand may be more complete. A disadvantage of a prospective cohort studymay include the long follow-up period while waiting for events ordiseases to occur. Thus, this study design is inefficient forinvestigating diseases with long latency periods and is vulnerable to ahigh loss to follow-up rate.

In contrast, retrospective cohort studies are better indicated fortimely and inexpensive study design. Retrospective cohort studies, alsoknown as historical cohort studies, are carried out at the present timeand look to the past to examine medical events or outcomes. A cohort ofsubjects, selected based on exposure status, is chosen at the presenttime, and outcome data (i.e. disease status, event status), which wasmeasured in the past, are reconstructed for analysis. An advantage ofthe retrospective study design analysis is the immediate access to thedata. The study design is comparatively less costly and shorter thanprospective cohort studies. However, disadvantages of retrospectivestudy design include limited control the investigator has over datacollection. The existing data may be incomplete, inaccurate, orinconsistently measured between subjects, for example, by not beinguniformly recorded for all subjects.

Conventionally, a cohort study defines the selected group of subjects bypredetermined criteria (e.g., exposure to a substance, or having aparticular medical condition, etc.) at the start of the investigation. Acritical characteristic of subject selection is to have both the exposedand unexposed groups be selected from the same source population.Subjects who are not at risk for developing the outcome should beexcluded from the study. The source population is determined bypractical considerations, such as sampling. Subjects may be effectivelysampled from the hospital, be members of a community, or from a doctor'sindividual practice. A subset of these subjects will be eligible for thestudy.

Attempts have been made and have failed to adequately address thecalculation of inferred selection criteria from an observed population.Attempts in the background art generally involve use set theoryvisualization to compare population across two attributes or datavariables. However, when population selection may involve as many as20-40 attributes, a set theory approach lacks scalability. Knownsolutions only allow comparison of two variables at a time and do notperform a population synthesis. Manual efforts to expand the analysisbeyond two variables has many drawbacks, such as requiring costly expertlabor to synthesize queries, being relatively slow, and is not adaptableto allow non-technical business users themselves to derive insights fromlarge healthcare datasets.

However, such selection methods for a retrospective cohort study maysuffer from limited sample size or selection bias. Therefore, what isneeded is to combine the advantages of a retrospective cohort studywithout the disadvantages of limited sample size or selection bias.

SUMMARY

Embodiments in accordance with the present disclosure provide asystematic process to determine the most significant factors that can beused to approximate a patient population group.

Embodiments in accordance with the present disclosure provide a methodto determine a reduced cohort criteria, the method including: defining Nselection criteria to select a cohort from among a universe of patientdata; querying a patient database, by use of a processor coupled to thepatient database, and by use of the N selection criteria, in order todefine a full patient population; selecting a subset of size M of the Nselection criteria, to produce a subset criteria; selecting apermutation of the subset criteria, to produce a permuted subsetcriteria in a predetermined order; for each member of the permutedsubset criteria: querying the patient database by use of the member ofthe permuted subset criteria to produce a respective interim patientpopulation; combining all respective interim patient populations toproduce a partial patient population; and calculating, by a processor, acoverage figure of merit that compares the partial patient population tothe full patient population.

A system to determine a reduced cohort criteria, the system including: acommunication interface to allow a human to define N selection criteriaused to select a cohort from among a universe of patient data; aprocessor coupled to a patient database, the processor configured toquery the patient database and by use of the N selection criteria, inorder to define a full patient population; a selection module coupled tothe processor, the selection module configured to select a subset ofsize M of the N selection criteria, to produce a subset criteria; aselection module coupled to the processor, the selection moduleconfigured to select a permutation of the subset criteria, to produce apermuted subset criteria in a predetermined order; for each member ofthe permuted subset criteria: querying the a patient database by use ofthe member of the permuted subset criteria to produce a respectiveinterim patient population; combining all respective interim patientpopulations to produce a partial patient population; and calculating, bya processor, a coverage figure of merit that compares the partialpatient population to the full patient population.

The preceding is a simplified summary of embodiments of the disclosureto provide an understanding of some aspects of the disclosure. Thissummary is neither an extensive nor exhaustive overview of thedisclosure and its various embodiments. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other embodimentsof the disclosure are possible utilizing, alone or in combination, oneor more of the features set forth above or described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and still further features and advantages of the presentinvention will become apparent upon consideration of the followingdetailed description of embodiments thereof, especially when taken inconjunction with the accompanying drawings wherein like referencenumerals in the various figures are utilized to designate likecomponents, and wherein:

FIG. 1 depicts a system according to an embodiment of the presentdisclosure;

FIG. 2 depicts a method according to an embodiment of the presentdisclosure; and

FIG. 3 illustrates components of a computing terminal;

FIG. 4 illustrates a user interface presented to a user to showselection criteria chosen by a user; and

FIG. 5 illustrates a user interface presented to a user to show a resultof a simulation using a selected subset and permutation of the selectioncriteria.

The headings used herein are for organizational purposes only and arenot meant to be used to limit the scope of the description or theclaims. As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). Similarly, the words“include”, “including”, and “includes” mean including but not limitedto. To facilitate understanding, like reference numerals have been used,where possible, to designate like elements common to the figures.Optional portions of the figures may be illustrated using dashed ordotted lines, unless the context of usage indicates otherwise.

DETAILED DESCRIPTION

The disclosure will be illustrated below in conjunction with anexemplary digital information system. Although well suited for use with,e.g., a system using a server(s) and/or database(s), the disclosure isnot limited to use with any particular type of system or configurationof system elements. Those skilled in the art will recognize that thedisclosed techniques may be used in any system or process in which it isdesirable to provide a transferable permission to access information orcontrol a decision.

The exemplary systems and methods of this disclosure will also bedescribed in relation to software, modules, and associated hardware.However, to avoid unnecessarily obscuring the present disclosure, thefollowing description omits well-known structures, components anddevices that may be shown in block diagram form, are well known, or areotherwise summarized.

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of embodiments orother examples described herein. In some instances, well-known methods,procedures, components and circuits have not been described in detail,so as to not obscure the following description. Further, the examplesdisclosed are for exemplary purposes only and other examples may beemployed in lieu of, or in combination with, the examples disclosed. Itshould also be noted the examples presented herein should not beconstrued as limiting of the scope of embodiments of the presentinvention, as other equally effective examples are possible and likely.

As used herein, the term “module” refers generally to a logical sequenceor association of steps, processes or components. For example, asoftware module may comprise a set of associated routines or subroutineswithin a computer program. Alternatively, a module may comprise asubstantially self-contained hardware device. A module may also comprisea logical set of processes irrespective of any software or hardwareimplementation.

As used herein, the term “transmitter” may generally comprise anydevice, circuit, or apparatus capable of transmitting a signal. As usedherein, the term “receiver” may generally comprise any device, circuit,or apparatus capable of receiving a signal. As used herein, the term“transceiver” may generally comprise any device, circuit, or apparatuscapable of transmitting and receiving a signal. As used herein, the term“signal” may include one or more of an electrical signal, a radiosignal, an optical signal, an acoustic signal, and so forth.

The term “computer-readable medium” as used herein refers to anytangible storage and/or transmission medium that participate in storingand/or providing instructions to a processor for execution. Such amedium may take many forms, including but not limited to, non-volatilemedia, volatile media, and transmission media. Non-volatile mediaincludes, for example, NVRAM, or magnetic or optical disks. Volatilemedia includes dynamic memory, such as main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, or any other magnetic medium,magneto-optical medium, a CD-ROM, any other optical medium, punch cards,paper tape, any other physical medium with patterns of holes, RAM, PROM,EPROM, FLASH-EPROM, solid state medium like a memory card, any othermemory chip or cartridge, a carrier wave as described hereinafter, orany other medium from which a computer can read. A digital fileattachment to e-mail or other self-contained information archive or setof archives is considered a distribution medium equivalent to a tangiblestorage medium. When the computer-readable media is configured as adatabase, it is to be understood that the database may be any type ofdatabase, such as relational, hierarchical, object-oriented, and/or thelike. Accordingly, the disclosure is considered to include a tangiblestorage medium or distribution medium and prior art-recognizedequivalents and successor media, in which the software implementationsof the present disclosure are stored.

At the present time, large-scale routine healthcare databases areamassed and maintained based upon data gathered by healthcare providersand healthcare insurers. For example, a patient who submits to routinehealth care such as a yearly checkup, regularly-scheduled pap smears ormammograms, or visits for acute but relatively minor problems such as aninfection, stitches, or broken bone, will have associated with them aseries of healthcare records over time. Healthcare records may alsoinclude information related to non-routine care such as emergency roomvisits, hospital admissions, or other serious healthcare events. Thehealthcare records may document the progress over time of chronicconditions such as cholesterol levels, high blood pressure, and thelike. The healthcare records may also include demographic informationsuch as age, ethnicity, height, weight, and so forth. Because a largeportion of the population has access to and uses health care, and theportion is likely to grow in future years due to the Affordable CareAct, such data is a vast source of information over a large portion orcross-section of the population, representing persons of many differentcharacteristics, risk factors, and so forth. The data for any individualpatient may also be available over an extended period of time such as aperiod of years, so that changes in slowly-progressing medicalconditions or slowly-changing patient characteristics may be captured bythe data.

In the United Kingdom (UK), such healthcare records may include sourcessuch as the Clinical Practice Research Datalink (CPRD), General PracticeResearch Database (GPRD), primary care database (GOLD), the hospitalepisode statistics (HES) and the Office for National Statistics (ONS)mortality data.

For example, the GPRD, established in the UK in 1987, is a medicalrecords database that general practitioners (GPs) use as the primarymeans of tracking patient clinical information. The total population inthe GPRD exceeds nine million patients with over 35 million person-yearsof follow-up between 1987 and 2002. About 5% of the UK population is inthe GPRD, which is broadly representative of the general UK populationin terms of age, sex and geographic distribution. The GPRD, whichcontains information on diagnoses and medications, was established withthe intent of allowing researchers to conduct high quality epidemiologicstudies and has been used in more than 200 peer-reviewed publications.All information is recorded by the GP or a member of the office staff aspart of the patient's medical record. Approximately 1,500 generalpractitioners representing 500 practices across the UK participated inthe GPRD between 1987 and 2001. GPs are trained in data entry and theirdata are reviewed by administrators at the GPRD to ensure that they areof sufficient quality for research studies.

Healthcare analysis and research increasing may rely upon the use ofsuch large-scale routine healthcare databases, in particular forretrospective cohort studies. Such databases, because of the coverageover a large portion or cross-section of the population, representingpersons of many different characteristics, risk factors, and so forth,may reduce the drawbacks of traditional retrospective cohort studiessuch as existing data being incomplete, inaccurate, or inconsistentlymeasured between subjects, for example, by not being uniformly recordedfor all subjects. Standardized tests for blood work, pap smear, andother routine procedures encourage uniformity and completeness ofmonitored healthcare parameters.

To work with large-scale routine healthcare databases for any use, thedefinition of the relevant population under study is the first step andan important step. There may be more than one relevant population, forexample, a first population that has developed a particular condition,and a second population that has not developed the particular conditionas of the time of selection. The selection criteria form an importantpart of protocols (i.e., population criteria and analysis plan) used forclinical trial and health outcomes studies.

The selection criteria may be referred to as a scenario. A scenario maybe known as a set of instructions used to define a patient cohort orsubpopulation from a database. A series of scenarios may be known as agroup of sets of instructions. Running a scenario may be known as thecomputational process of applying a set of instructions to a database.For example, suppose a database contains information about a largenumber of patients with asthma. Further suppose that we want to studypatients who were originally diagnosed with asthma as children. However,suppose we need to better define the term “child”, because a patientdiagnosed as a teenager may be medically treated differently than apatient diagnosed earlier than age 12. Therefore, we may run a fewscenarios in order to test for an appropriate age range. In a firstscenario, we may include everybody diagnosed under 18 years of age. In asecond scenario, we may include everybody between 3-17 years old. In athird scenario, we may include everybody between 12-17. Thus, we mayhave a set of three different scenarios. Sending the instruction of“Return every patient in the age range X-Y” to the database is anexample of the process of running the scenario.

Patient characteristics may be represented as a multi-dimensional vectoror matrix. Patient characteristics may include sociodemographic factors(e.g., age, sex, place of residence, etc.), clinical factors (e.g.,comorbidities, medical history, genetic history, blood type, medicationsused in the week prior to presentation, functional status, immunizationhistory, smoking status, drinking status, etc.), and laboratory data.Dozens of characteristics may be relevant or possibly relevant.Relevancy may be dependent upon the type of study, and may be informedby existing medical knowledge. For example, patient weight may be morerelevant to a diabetes study than patient eye characteristics, butpatient eye characteristics may have more relevance to a study of eyedisease.

Each patient characteristic over a population of patients may beexpressed as a statistic that represents the population as a whole. Forexample, the statistic may be in a form such as a histogram, a series ofnumeric ranges (e.g., 40-50 years old; 50-60 years old; 150-160 lbs;160-170 lbs; etc), a series of qualitative ranges (e.g., non-drinker vs.social drinker vs. heavy drinker, etc.), and so forth. Othermathematical representations of the multi-dimensional vector or matrixmay be possible. Patient characteristics may not be independent of eachother, e.g., selection of a female gender characteristic may result in asmaller and lighter population of patients compared to a selection of amale gender characteristic. The data is complex and highly dimensional.Researchers have to make assumptions, based upon science, intuition orother data analysis, that involve structure that is believed to exist inthe data but that cannot be observed directly. The data sets are largeand growing with a never-ending stream of new data.

Some patients may be classified by use of one or more population codes.The population codes, in turn, represent characteristics of interest toa retrospective cohort study. For example, one population coding systemis ICD-10, which is the 10th revision of the International StatisticalClassification of Diseases and Related Health Problems (ICD), a medicalclassification list by the World Health Organization (WHO). ICD-10 codesfor diseases, signs and symptoms, abnormal findings, complaints, socialcircumstances, and external causes of injury or diseases. The code setallows more than 14,400 different codes and permits the tracking of manynew diagnoses. The codes can be expanded to over 16,000 codes by usingoptional sub-classifications. The detail reported by ICD can be furtherincreased, with a simplified multi-axial approach, by using codes meantto be reported in a separate data field.

Another population coding system is the Read code, which is the standardclinical terminology system used in General Practice in the UnitedKingdom (UK). Read codes support detailed clinical encoding of multiplepatient phenomena including: occupation; social circumstances; ethnicityand religion; clinical signs, symptoms and observations; laboratorytests and results; diagnoses; diagnostic, therapeutic or surgicalprocedures performed; and a variety of administrative items (e.g.whether a screening recall has been sent and by what communicationmodality, or whether an item of service fee has been claimed). Ittherefore includes but goes significantly beyond the expressivity of adiagnosis coding system.

However, there are major barriers to usage of coding systems for patientcharacteristics and capturing classification value from large-scaleroutine healthcare databases. Due the complex nature of population codesused to define populations, conventional practice has been that onlyexperts in the use of large healthcare datasets, who possess technicalskills with respect to statistical software concepts and usage, canmanually define and understand such population codes in practice.

Conventionally, synthesis of population selection rules also must beperformed manually by such an expert. Synthesis is known as a process ofreducing from potentially hundreds of patient population codes to a muchsmaller set of medical factors, the factors being referred to asinclusion factors or exclusion factors. For example, for a predeterminedasthma population (e.g., patients that were initially diagnosed between12-17 years of age) a medical researcher may decide to look at onlypatients who were treated with either of two drugs: inhaledcorticosteroids (ICS) or fluticasone (i.e., an example of an inclusioncriterion). Each of those drugs will have a specific code which usuallyless recognizable to medical researchers than the drug name itself. Inaddition to looking at these drugs, a medical researcher may also setanother rule to study only patients who were treated in a primary caresetting. However, in practice a rule to narrow a study only to patientswho were treated in a primary care setting may not be significantbecause virtually all asthma patients are treated in a primary caresetting and thus fails to narrow the population much in practice. Manualsynthesis may fail to recognize that such a rule is not significantlymeaningful. Thus, manual synthesis may include such a criterion whereasan automated method may recognize that the criterion is notsignificantly meaningful and thus would not include the criterion in asummary.

Inclusion factors refer to the medical factors whose presence is mosthighly correlated (e.g., in a mathematical sense) with the selectedpatients. Exclusion factors refer to the medical factors whose absenceis most highly correlated with the selected patients. Inclusion andexclusion factors may be scaled to a mathematical range, e.g., [−1.0,1.0] for a mathematical correlation, or [0%, 100%], and so forth.Inclusion and exclusion factors when interpreted may be normalized totheir presence in a population of patients, e.g., an unnormalizedinclusion factor of 20% may be more significant after normalization if adifferent group (e.g., a control group or the general population)contain a much smaller number (e.g., 1%) of members who share thatfactor. Manual synthesis may represent days or weeks of effort,depending upon the size of the study. Persons who possess such skillsare scarce in many countries. Therefore, a need exists for systems thatcan analyze population groups in an automated manner, with little to noinput required of a human expert.

Manual synthesis may result in a large number of rules (i.e., inclusionand exclusion criteria) that are used, even though only a few criteriahave significant impact on the population selection. A large number ofcriteria that have little impact to patient selection may tend toobscure insights gained from consideration of just the most significantcriteria, in which significance is determined by how much the patientpopulation is narrowed.

Embodiments in accordance with the present disclosure improve on thismanual synthesis. The number of patients in the final cohort will stayidentical and is independent of the order in which the criteria areapplied. However, the present Applicants have discovered that thedifferent criteria may have different impacts depending on the sequenceof application. For a first example, suppose we are studying femalebreast cancer and the first criterion we apply a gender criterion toproduce an immediate 50% reduction in the population. In contrast, for asecond example suppose that the first criterion we apply to thepopulation is “Diagnosis: Breast cancer” and then we apply as a secondcriterion the gender specification “Women.” In the second example, thegender specification would have hardly any impact on the size of theoverall cohort, because even though breast cancer in men is not unknown,breast cancer is overwhelmingly more prevalent in women than in men.

More generally, when a population (i.e., a patient cohort) isconstructed, embodiments in accordance with the present disclosure maytest various combinations of the patient population codes and/or theirassociated patient criteria on a dataset through simulations to find outwhich inclusion and exclusion criteria drove the majority of theselection. The simulation is the process of defining the key criteriaranking. In contrast, synthesis builds upon simulation by also includingthe decision of which factors to show in an output (e.g., for a humanresearcher). The automated synthesis may take from hours to days ofsimulation work, but may save days to weeks of manual synthesis, andfurthermore may allow for the analysis of additional scenarios thatcould not feasibly be analyzed by manual methods within a desiredanalytic timeframe. The simulation results may be saved for future oroff-line analysis.

Embodiments in accordance with the present disclosure may furthersynthesize in an automated manner, and through intuitive visualrepresentation, the most critical rules, which hither-to had to beconducted by experienced experts.

Criteria may be combined using a combination of both Boolean “AND” andBoolean “OR” operations. For example, suppose the total patientpopulation may be defined by the five criteria: (“A” and “B” and “C” and(“D” or “E”)). In a reduced set of criteria, omitting one of the “AND”factors (e.g., use only: (“B” and “C” and (“D” or “E”)), results in alarger set than the total patient population we are trying to emulate,i.e., over-inclusion. Omitting one of the “OR” factors (e.g., use only“A” and “B” and “C” and “D”), results in a smaller set than the totalpatient population we are trying to emulate, i.e., under-inclusion. Insome embodiments, over-inclusions may be weighted differently thanunder-inclusions for the purpose of determining how well a synthesizedpopulation represents a full patient population. In some embodiments, ahigher degree of specificity is preferred over greater quantity, soover- inclusion would be weighted to discourage its occurrence relativeto under-inclusion.

After embodiments simulate various combinations of patient populationcodes and/or the associated patient criteria, a synthesis of the codescan be provided (e.g., displayed) to allow both experts and non-expertsto determine which specific factors drove the majority of the populationselection. For example, it may become apparent that a cohort sharingsixteen patient criteria may turn out to have three patient criteriathat determined the majority of population selection. Such determinationis common in many population selections.

A simulation may proceed as follows. First, a user defines cohortcriteria such as an age band or treatment (i.e., examples of controlledvariables of the simulation), which are applied to the population indifferent sequences. Suppose that there are a total of “N” criteria (N apositive integer). It is well known from combinatorial mathematics thatthe number of permutations of N items is N! (i.e., N factorial).However, not all of the N criteria may be significant, e.g., N−1 or N−2(or fewer) criteria may produce acceptable results, meaning that (N−1)!and (N−2)! permutations (as well as lesser numbers of permutations) mayalso need to be analyzed. The total number of permutations may be aslarge as Σ_(i=1) ^(i=N−1) (N−i)! . Because thirty or more of suchcriteria may be used in the definition of a cohort (i.e., N>=30),comprehensive manual testing of potential sequences is impractical.

A system in accordance with an embodiment of the present disclosuredetermines a cohort group for a predetermined number of criteria andorder of application of the criteria. The embodiments then log theresulting cohort group (e.g., number of patients) associated with eachof the different sequences that were chosen. At the conclusion of thesimulation, one or more reduced sets of criteria may be outputted inorder to produce the final cohort. Several top options may be presentedto offer a tradeoff between the coverage and number of criteria. Forexample, if four factors produce 99% coverage and five factors produce99.5% coverage, both may be presented to a human researcher or analystfor consideration. A sequence under test may be deemed to be good enoughif the cohort produced by the sequence under test is sufficiently closein membership (e.g., by comparison of population numbers) to a cohortproduced by consideration of all N criteria.

Embodiments in accordance with the present disclosure automate thesynthesis of a population of patients under study along with thecalculation of inferred or deduced selection criteria, and display thepopulation and selection criteria graphically along with key informationabout the inferred or deduced selection criteria. Embodiments advanceaccess to insights from large healthcare datasets for non-experts, andfacilitate unlocking use of these insights regarding the data, toimprove healthcare efficiency.

FIG. 1 depicts a system 100 according to an embodiment of the presentdisclosure. The system 100 may include a communication network 108 thatis in communication with computing terminal 112. Exemplary types ofexternal communication devices 112 include, without limitation, desktopPersonal Computers (PCs), laptops, netbooks, tablets, thin clients,other smart computing devices, and the like that are accessible via anetwork. The communication link may operate by methods or protocols suchas Ethernet, Wi-Fi, and so forth. The computing power of computingterminal 112 may be used at least in part to manage communications withother portions of system 100 described below.

The communication network 108 may be packet-switched and/orcircuit-switched. An exemplary communication network 108 includes,without limitation, a Wide Area Network (WAN), such as the Internet, aPublic Switched Telephone Network (PSTN), a Plain Old Telephone Service(POTS) network, a cellular communications network, or combinationsthereof. In one configuration, the communication network 108 is a publicnetwork supporting the TCP/IP suite of protocols.

System 100 may further include server 144, which is coupled tocommunication network via transceiver 146. Transceiver 146 may supportwell-known communication or networking protocols such as Ethernet,Wi-Fi, and so forth. Server 144 may be capable of hosting and/orexecuting one or more application programs 152 (“apps” or“applications”). Server 144 may be a software-controlled systemincluding a processor 154 coupled to a tangible memory 156. Memory 156may comprise random access memory (RAM), a read-only memory (ROM), orcombinations of these and other types of electronic memory devices.Memory 156 may be used for various purposes such as to store code (e.g.,application programs 152) and working memory used by processor 154.Various other server 144 components such as a communication interfacemodules, power management modules, etc. are known by persons of skill inthe art of computer design, but are not depicted in FIG. 1 in order toavoid obscuring the main elements of system 100.

Server 144 may be coupled to a database 162, either directly or throughcommunication network 108 as illustrated in FIG. 1. Database 162 mayalso be separate from server 144 (as illustrated in FIG. 1), or beincorporated into server 144. Database 162 may be used to store anavailable universe of patient data (e.g., the GPRD). Database 162 mayrepresent a plurality of physically dispersed databases that arecommunicatively coupled together.

The elements of system 100 are shown in FIG. 1 for purposes ofillustration only and should not be construed as limiting embodiments ofthe present invention to any particular arrangement of elements. Variousother system components such as a gateway, a firewall, etc. are known bypersons of skill in the art of computer networking, but are not depictedin FIG. 1 in order to avoid obscuring the main elements of system 100.

FIG. 2 illustrates a method 200 to operate system 100, in accordancewith an embodiment of the present disclosure. Method 200 may proceed asfollows. At step 201, a researcher or user of system 100 (generically,“user”) may access system 100 from computing terminal 112 in order todefine a selection criteria (i.e., a scenario) as part of a study.Without loss of generality, assume that there are “N” criteria.

Next, control of method 200 transitions to step 203 at which thescenario will be run (e.g., by the user at computing terminal 112) inorder to determine a full patient population that satisfies theselection criteria. After this step, an automated synthesis process maybe initiated (e.g., by a user). Computations may be performed by server144 using data read from database 162.

Next, control of method 200 transitions to step 205, which begins theautomatic synthesis process. The steps of the automatic synthesisprocess are encoded by software commands stored in memory 156 (e.g., asone or more apps 152), and are carried out by processor 154 performingactions as commanded by app 152.

The automated synthesis process beginning at step 205 and through step225 will test various subsets and permutations of the N selectioncriteria, and record which subsets and permutations thereof of the Nselection criteria produces a partial patient population that issufficiently close to the full patient population. A comprehensiveanalysis may analyze every possible subset and permutation thereof ofthe N selection criteria, and will find a globally optimal solution. Adisadvantage of a comprehensive analysis is that the simulation may takea long time to run. A selective analysis may analyze selected subsetsand permutations thereof of the N selection criteria. An advantage ofthe selective analysis is that it is faster but a disadvantage is thatis may miss finding the globally best solution.

“Partial” patient population refers to an observation that a patientpopulation produced by omitting some of the N selection criteria ingeneral will be different than a patient population produced by all Nselection criteria. For example, if the omitted selection criterion hadbeen specified by a Boolean “AND”, then the partial patient populationwill be larger than the full patient population. If the omittedselection criterion had been specified by a Boolean “OR”, then thepartial patient population will be smaller than the full patientpopulation. “Coverage” as used herein is a figure of merit that mayrefer to how close the partial patient population comes to the fullpatient population. Coverage may be calculated by one or morestatistics, such as what percentage of the full patient population isincluded (or not included) in the partial patient population, and/orwhat percentage of the partial patient population is included (or notincluded) in the full patient population.

The automated synthesis process may proceed in one of several ways. Forexample, in an ascending method, the synthesis process may begin withsmall subsets and progressively analyze larger subsets of the Nselection criteria. In a descending method, the synthesis may begin withan (N−1)-member subset of the selection criteria, analyzing all possiblepermutations thereof, and then moving on to progressively smallersubsets thereof. Ascending and descending methods may be used with bothcomprehensive and partial analysis.

For example, an ascending method may begin with calculating coverage foreach of the N selection criteria individually. Any coverages that meetor exceed a threshold level of coverage may cause a signal to be raised(e.g., by an alert module or the like), which may trigger recording tomemory of the coverage and corresponding subset and permutation ofselection criteria that produced the coverage. Alternatively, only theK-best coverages (K≧1) calculated during the entire simulation may beretained. During the running of the simulation, detection of one of theK-best coverages up to that point in the simulation may cause a signalto be raised, which may trigger recording to memory of the coverage andcorresponding subset and permutation of selection criteria that producedthe coverage. A record of the K-best coverages may be maintained as arunning record that is updated as necessary at the end of testing eachsubset and permutation, or may be calculated at the end of the entiresimulation.

The signals may also be used as an alert to the human analyst who isresponsible for the simulation. Having available more than onesufficiently acceptable permutation of selection criteria may be usefulat the conclusion of the simulation in order for a human analyst tostudy further the appropriate selection criteria. The human analyst maylater change the selection criteria for a new simulation. The humananalyst may select an acceptable subset of criteria and permutationbased upon considerations not considered or captured by the simulation.

After each of the N selection criteria have been analyzed one at a time,the ascending method may then continue by analyzing every possible pairof the N selection criteria. For example, in a comprehensive ascendingmethod, if each of the N selection criteria are denoted C_(n), 1≦n≦N,then each combination and permutation of pairs (C_(i), C_(j)) ∀1≦i≦N,1≦j≦N, i≠j is analyzed, and any coverages and corresponding criteriathat meet or exceed a threshold level of coverage may be recorded. Afterall possible combinations and permutations of pairs (C_(i), C_(j)) areanalyzed, the comprehensive ascending method may continue by analyzingall possible combinations and permutations of triplets (C_(i), C_(j),C_(k)) of the N selection criteria. This process continues until allpossible combinations and permutations of subsets of size (N−1) elementsare analyzed.

An ascending method may be used with a partial analysis. For example,after a predetermined number of stages, the subset and permutationproducing the best coverage may be outputted as a partial result. Thecriteria forming the partial result may be removed from furtherconsideration, and the rest of the remaining criteria may be analyzed tofind the best additional coverage from the remaining criteria. Forexample, if after the third stage the best subset and permutation ofcriteria is (C₅, C₁, C₂), then criteria C₁, C₂, C₅ may be removed fromfurther consideration and analysis will continue using only criteria C₃,C₄, and C₆ . . . C_(N). In this example, a subset of size J=3 of theoriginal N criteria has been identified, and an intermediate permutation(C₅, C₁, C₂) of the J criteria has been identified. The J criteria havebeen removed from further consideration as being an intermediate result,and the analysis may proceed using the N−J remaining criteria (i.e., C₃,C₄, and C₆ . . . C_(N)). Additional intermediate results may also betaken of the N−J remaining criteria. At the end of the analysis,intermediate result(s) may be concatenated with the results based uponthe N−J remaining criteria, to produce overall results. Other methods ofpruning may also be used.

A descending method may begin by analyzing all possible permutations ofsmaller and smaller subsets of (N−1) selection criteria and fewer. Thereis no need to examine permutations of all N selection criteria, since itis known that each such permutation will result in the full patientpopulation. For example, for (N−1) selection criteria, the set of C₁ . .. C_(N) criteria (except for the one the removed criterion) forms asubset, and the subset may be permuted and a coverage may be calculatedfor each permutation of the subset. In other respects, the descendingmethod may operate similarly to the ascending method as described above.

Since even for a large number of criteria C_(N) it is likely that only arelatively small number of criteria may be used to provide an acceptablelevel of coverage, an ascending method is likely to be computationallymore efficient, because the smallest subset of criteria producing anacceptable coverage is likely to be found more quickly.

Returning again to FIG. 2, method 200 at step 205 will select a subset“M” of the N selection criteria. For an ascending method, step 205begins with M=1. For a descending method, step 205 begins with M=N−1.

Next, control of method 200 transitions to step 207, at which apermutation of the M selection criteria is selected. There will be Mfactorial number of permutations from which to select.

Next, control of method 200 transitions to step 209, at which a partialpatient population for the selected permutation of M selection criteriais calculated.

Next, control of method 200 transitions to step 211, at which a coverageresulting from the selected permutation of M selection criteria iscalculated. The coverage is a statistic that indicates how close thepartial patient population is to the full patient population. Thestatistic may be in the form of, e.g., a percentage of overlap ornon-overlap, a mathematical correlation, and so forth. The statistic mayfurther show a relative contribution to coverage from each of thecriteria. The coverage is then compared to a threshold, for example,that at least 99.5% of full patient population is included in thepartial patient population. If the coverage exceeds the threshold thencontrol of method 200 transitions to step 213, otherwise control ofmethod 200 transitions directly to step 215.

At step 213, a record may be stored in memory 156 of the coverage andthe selected permutation of the M selection criteria that produced thecoverage. In some embodiments, all permutations that exceed thethreshold may be recorded and retained. In some embodiments, only thehighest K permutations (K≧1 may be retained. At the conclusion of step213, control of method 200 transitions to step 215.

At decision step 215, a decision is made and a resulting signal is setto indicate whether the last permutation for the present set of Mselection criteria that were selected at step 207 has been analyzed. Ifthe outcome of decision step 215 is negative, then control of method 200transitions to step 217 at which a next permutation for the present setof M selection criteria is selected for analysis to calculate a partialpatient population. For example, if N=5 and M=3, and if the presentsubset and permutation is (C₂, C₁, C₄), then the next permutation may be(C₂, C₄, C₁). At the conclusion of step 217, control of method 200reverts to step 209. If the outcome of decision step 215 is positive,then control of method 200 transitions to step 219.

At decision step 219, a decision is made and a resulting signal is setto indicate whether the last subset M of the N selection criteria havebeen considered and analyzed. If the outcome of decision step 219 isnegative, then control of method 200 transitions to step 221 at which anext subset M of the N selection criteria is considered. For example, ifN=5 and M=3, and if the present subset is (C₁, C₂, C₄), then the nextsubset may be (C₁, C₂, C₅). At the conclusion of step 221, control ofmethod 200 reverts to step 207. If the outcome of decision step 219 ispositive, then control of method 200 transitions to step 223.

At decision step 223, a decision is made and a resulting signal is setto indicate whether the last value of M has been considered. If theoutcome of decision step 223 is negative, then control of method 200transitions to step 225 at which a next value of M is considered. Forexample, if N=5 and M=3, and all permutations of the five criteria takenthree at a time have been considered, then at step 225 the next value ofM may be considered. For an ascending method, the next value after M=3would be M=4. For a descending method, the next value after M=3 would beM=2. At the conclusion of step 225, control of method 200 reverts tostep 205. If the outcome of decision step 223 is positive, then method200 ends.

FIG. 3 illustrates components of computing terminal 112. As illustrated,in this embodiment, computing terminal 112 is a typical desktop ormobile computing device having basic functions. Computing terminal 112has a user input interface 251 for receiving input from a user (e.g., akeyboard, touchscreen and/or microphone), and a user output interface253 is provided for presenting information visually or audibly to theuser. Computing terminal 112 also includes memory 255 for storing anoperating system that controls the main functionality of computingterminal 112, along with a number of applications that are run oncomputing terminal 112, and data. A processor 257 executes the operatingsystem and applications. Computing terminal 112 may have a uniquehardware identification code that permits identification of computingterminal 112 (e.g., a medium access control (MAC) address). At least aportion of memory 255 may be encrypted. A communications interface 259permits communications with communication network 108, e.g., by way ofan Ethernet or Wi-Fi interface. A user may use computing terminal 112 inorder to control the practice of embodiments described herein, and toreceive and review results of the embodiments.

FIG. 4 illustrates a cohort detailed description 400 that system 100 maypresent to a user, e.g., at computing terminal 112, which shows variousselection criteria (i.e., filters) chosen by a user for a cohort studyand other information relevant to the cohort study. Application of allof these selection criteria would produce a full patient population.

FIG. 5 illustrates a user interface 500 that system 100 may present to auser, e.g., at computing terminal 112, which shows the result of amanual synthesis of selection criteria, and running a scenario using allof the selection criteria. User interface 500 is provided forillustration only, and does not necessarily correspond specifically tocohort detailed description 400. User interface 500 illustrates a fullpatient population using all of the selection criteria, including apercentage inclusion for each criterion. The percentages of inclusionprovide to a human analyst an understanding of the relative importanceof various selection criteria to the overall full patient population. Ifa human analyst wanted to reduce the number of criteria, while providinga partial patient population that is sufficiently close to the fullpatient population, an understanding of the relative importance ofvarious selection criteria would be helpful if the process simply were,e.g., a greedy process that selected the N-best (i.e., highestpercentage) criteria. However, this would give no insight as to theorder in which to apply criteria, and furthermore a greedy process maymiss a globally optimal solution. Embodiments in accordance with thepresent disclosure can overcome this problem by comprehensively studyingall possible number of factors, selections of factors, and permutationsof factors.

Embodiments of the present invention include a system having one or moreprocessing units coupled to one or more memories. The one or morememories may be configured to store software that, when executed by theone or more processing unit, allows practice of embodiments describedherein, including at least in FIG. 2 and related text.

The disclosed methods may be readily implemented in software, such as byusing object or object-oriented software development environments thatprovide portable source code that can be used on a variety of computeror workstation platforms. Alternatively, the disclosed system may beimplemented partially or fully in hardware, such as by using standardlogic circuits or VLSI design. Whether software or hardware may be usedto implement the systems in accordance with various embodiments of thepresent invention may be dependent on various considerations, such asthe speed or efficiency requirements of the system, the particularfunction, and the particular software or hardware systems beingutilized.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the present invention may be devisedwithout departing from the basic scope thereof. It is understood thatvarious embodiments described herein may be utilized in combination withany other embodiment described, without departing from the scopecontained herein. Further, the foregoing description is not intended tobe exhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention. Certainexemplary embodiments may be identified by use of an open-ended listthat includes wording to indicate that the list items are representativeof the embodiments and that the list is not intended to represent aclosed list exclusive of further embodiments. Such wording may include“e.g.,” “etc.,” “such as,” “for example,” “and so forth,” “and thelike,” etc., and other wording as will be apparent from the surroundingcontext.

No element, act, or instruction used in the description of the presentapplication should be construed as critical or essential to theinvention unless explicitly described as such. Also, as used herein, thearticle “a” is intended to include one or more items. Where only oneitem is intended, the term “one” or similar language is used. Further,the terms “any of” followed by a listing of a plurality of items and/ora plurality of categories of items, as used herein, are intended toinclude “any of,” “any combination of,” “any multiple of,” and/or “anycombination of multiples of” the items and/or the categories of items,individually or in conjunction with other items and/or other categoriesof items.

Moreover, the claims should not be read as limited to the describedorder or elements unless stated to that effect. In addition, use of theterm “means” in any claim is intended to invoke 35 U.S.C. §112, ¶6, andany claim without the word “means” is not so intended.

What is claimed is:
 1. A method to determine a reduced cohort criteria,comprising: defining N selection criteria to select a cohort from amonga universe of patient data; querying a patient database, by use of aprocessor coupled to the patient database, and by use of the N selectioncriteria, in order to define a full patient population; selecting asubset of size M of the N selection criteria, to produce a subsetcriteria; selecting a permutation of the subset criteria, to produce apermuted subset criteria in a predetermined order; for each member ofthe permuted subset criteria: querying the patient database by use ofthe member of the permuted subset criteria to produce a respectiveinterim patient population; combining all respective interim patientpopulations to produce a partial patient population; and calculating, bya processor, a coverage figure of merit that compares the partialpatient population to the full patient population.
 2. The method ofclaim 1, further comprising the step of: repeating the step of selectinga permutation for another permutation of the subset of size M.
 3. Themethod of claim 1, further comprising the step of: repeating the step ofselecting a subset of size M for another subset of size M.
 4. The methodof claim 1, further comprising the step of: repeating the step ofselecting a subset of size M for another size M of the subset.
 5. Themethod of claim 1, further comprising the step of: raising a signal ifthe coverage figure of merit exceeds a predetermined threshold.
 6. Themethod of claim 1, further comprising the step of: raising a signal ifthe coverage figure of merit is among a predetermined K-best values ofthe figure of merit.
 7. The method of claim 1, further comprising thesteps of: repeating the step of selecting a permutation for allpermutations of the M selected subset criteria; repeating the step ofselecting a subset of size M for all subsets of size M; and repeatingthe step of selecting all subsets of size M for all integral sizes M ofthe subset, for 1≦M<N.
 8. The method of claim 1, further comprising thestep of: calculating intermediate results; pruning criteria based uponthe intermediate results to produced a reduced set; and analyzing thereduced set.
 9. The method of claim 1, further comprising the steps of:identifying an intermediate subset of size J of the criteria;identifying an intermediate permutation of the J criteria; removing theJ criteria from the set of N criteria, to produce N−J remainingcriteria; sequentially selecting all subsets of size M_(J) for allintegral sizes M_(J) of the subset, for 1≦M_(J)<N−J. sequentiallyselecting a subset of size M_(J) for all subsets of size M_(J), toproduce a respective subset remaining criteria; sequentially selecting apermutation for all permutations of the M_(J) selected subset remainingcriteria, to produce a permuted subset remaining criteria in apredetermined order; for each member of the permuted subset remainingcriteria: querying a patient database by use of the member of thepermuted subset remaining criteria to produce a respective interimpatient population; combining all respective interim patient populationsto produce a partial patient population; and calculating, by aprocessor, a coverage figure of merit that compares the partial patientpopulation to the full patient population.
 10. A system to determine areduced cohort criteria, comprising: a communication interface to allowa human to define N selection criteria used to select a cohort fromamong a universe of patient data; a processor coupled to a patientdatabase, the processor configured to query the patient database and byuse of the N selection criteria, in order to define a full patientpopulation; a selection module coupled to the processor, the selectionmodule configured to select a subset of size M of the N selectioncriteria, to produce a subset criteria; a selection module coupled tothe processor, the selection module configured to select a permutationof the subset criteria, to produce a permuted subset criteria in apredetermined order; and for each member of the permuted subsetcriteria: querying the a patient database by use of the member of thepermuted subset criteria to produce a respective interim patientpopulation; combining all respective interim patient populations toproduce a partial patient population; and calculating, by a processor, acoverage figure of merit that compares the partial patient population tothe full patient population.
 11. The system of claim 10, wherein theselection module selects another permutation of the subset of size M.12. The system of claim 10, wherein the selection module selects anothersubset of size M.
 13. The system of claim 10, wherein the selectionmodule selects another size M of the subset.
 14. The system of claim 10,an alert module further configured to raise a signal if the coveragefigure of merit exceeds a predetermined threshold.
 15. The system ofclaim 10, an alert module further configured to raise a signal if thecoverage figure of merit is among a predetermined K-best values of thefigure of merit.
 16. The system of claim 10, wherein the processor isconfigured: to repeat selection of a permutation for all permutations ofthe M selected subset criteria; to repeat selection of a subset of sizeM for all subsets of size M; and to repeat selection of all subsets ofsize M for all integral sizes M of the subset, for 1≦M<N.
 17. The systemof claim 10, further comprising: a calculation module configured tocalculate intermediate results; a pruning module configured to prunecriteria based upon the intermediate results to produced a reduced set;and an analysts module configured to analyze the reduced set.